0c6fd1703e
Adds a basic training script for a simple audio model to our examples. See third_party/docs_src/tutorials/audio_recognition.md for full documentation PiperOrigin-RevId: 165025732
128 lines
5.2 KiB
C++
128 lines
5.2 KiB
C++
/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include "tensorflow/examples/speech_commands/recognize_commands.h"
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namespace tensorflow {
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RecognizeCommands::RecognizeCommands(const std::vector<string>& labels,
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int32 average_window_duration_ms,
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float detection_threshold,
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int32 suppression_ms, int32 minimum_count)
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: labels_(labels),
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average_window_duration_ms_(average_window_duration_ms),
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detection_threshold_(detection_threshold),
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suppression_ms_(suppression_ms),
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minimum_count_(minimum_count) {
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labels_count_ = labels.size();
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previous_top_label_ = "_silence_";
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previous_top_label_time_ = std::numeric_limits<int64>::min();
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}
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Status RecognizeCommands::ProcessLatestResults(const Tensor& latest_results,
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const int64 current_time_ms,
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string* found_command,
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float* score,
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bool* is_new_command) {
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if (latest_results.NumElements() != labels_count_) {
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return errors::InvalidArgument(
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"The results for recognition should contain ", labels_count_,
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" elements, but there are ", latest_results.NumElements());
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}
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if ((!previous_results_.empty()) &&
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(current_time_ms < previous_results_.front().first)) {
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return errors::InvalidArgument(
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"Results must be fed in increasing time order, but received a "
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"timestamp of ",
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current_time_ms, " that was earlier than the previous one of ",
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previous_results_.front().first);
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}
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// Add the latest results to the head of the queue.
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previous_results_.push_back({current_time_ms, latest_results});
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// Prune any earlier results that are too old for the averaging window.
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const int64 time_limit = current_time_ms - average_window_duration_ms_;
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while (previous_results_.front().first < time_limit) {
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previous_results_.pop_front();
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}
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// If there are too few results, assume the result will be unreliable and
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// bail.
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const int64 how_many_results = previous_results_.size();
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const int64 earliest_time = previous_results_.front().first;
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const int64 samples_duration = current_time_ms - earliest_time;
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if ((how_many_results < minimum_count_) ||
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(samples_duration < (average_window_duration_ms_ / 4))) {
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*found_command = previous_top_label_;
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*score = 0.0f;
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*is_new_command = false;
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return Status::OK();
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}
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// Calculate the average score across all the results in the window.
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std::vector<float> average_scores(labels_count_);
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for (const auto& previous_result : previous_results_) {
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const Tensor& scores_tensor = previous_result.second;
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auto scores_flat = scores_tensor.flat<float>();
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for (int i = 0; i < scores_flat.size(); ++i) {
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average_scores[i] += scores_flat(i) / how_many_results;
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}
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}
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// Sort the averaged results in descending score order.
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std::vector<std::pair<int, float>> sorted_average_scores;
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sorted_average_scores.reserve(labels_count_);
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for (int i = 0; i < labels_count_; ++i) {
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sorted_average_scores.push_back(
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std::pair<int, float>({i, average_scores[i]}));
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}
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std::sort(sorted_average_scores.begin(), sorted_average_scores.end(),
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[](const std::pair<int, float>& left,
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const std::pair<int, float>& right) {
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return left.second > right.second;
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});
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// See if the latest top score is enough to trigger a detection.
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const int current_top_index = sorted_average_scores[0].first;
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const string current_top_label = labels_[current_top_index];
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const float current_top_score = sorted_average_scores[0].second;
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// If we've recently had another label trigger, assume one that occurs too
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// soon afterwards is a bad result.
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int64 time_since_last_top;
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if ((previous_top_label_ == "_silence_") ||
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(previous_top_label_time_ == std::numeric_limits<int64>::min())) {
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time_since_last_top = std::numeric_limits<int64>::max();
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} else {
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time_since_last_top = current_time_ms - previous_top_label_time_;
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}
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if ((current_top_score > detection_threshold_) &&
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(current_top_label != previous_top_label_) &&
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(time_since_last_top > suppression_ms_)) {
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previous_top_label_ = current_top_label;
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previous_top_label_time_ = current_time_ms;
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*is_new_command = true;
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} else {
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*is_new_command = false;
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}
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*found_command = current_top_label;
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*score = current_top_score;
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return Status::OK();
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}
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} // namespace tensorflow
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